Attribution modeling is a practice in which one or more of a user's online actions are given the credit for influencing a particular desired user action. A desired user action may be referred to as a conversion or success event. Attribution modeling may be applied to various types of online activity, from display ads on search engines, to search keywords, to “liking” social media blog entries, to views of individual web pages. For instance, if a user performs three internal searches for kw1, kw2, and kw3 in that order, and then makes a purchase, attribution modeling may be used to determine which search term deserves credit for influencing the purchase. Thus, an attribution model may be considered a rule that determines how credit for a success event (i.e., desired user action), such as an online purchase, may be assigned to one or more of the actions a user performed prior to performing the success event.
Some common attribution models include Last Touch, First Touch, and Linear, among others. In a Last Touch attribution model, the last user action prior to the success event would be given all of the credit for influencing the success event. A Last Touch model assumes that the last thing a user did before converting had the largest influence on the user. In the online purchase example above, kw3 (e.g., the last internal keyword search) would be given the credit for influencing the user to make the purchase.
In a First Touch attribution model, the first action to occur receives the credit for the success event. This model assumes that the first action of a user deserves credit for influencing the user in the subsequent actions, including the success event. In the online purchase example above, kw1 (e.g., the first internal keyword search) would be given the credit for influencing the user to make the purchase.
In a Linear attribution model, all actions prior to a success event get an equal share of the credit for influencing the user. Rather than assuming that a single one of the user's actions deserves all the credit, this model assumes that any, or all, of the user's actions influence the user. In the online purchase example above, each of the three keywords (kw1, kw2 and kw3) would be given a third of the credit for influencing the user to make the purchase.
None of the above attribution models may be 100% accurate and each model may have individual disadvantages that apply in different situations or scenarios. Each of these model rely on the assumption that over time (i.e., across multiple success events) the stronger influences may become apparent.